提出了一种采用近红外光谱快速鉴别转基因小麦种子的方法, 对不同品种的9个转基因小麦样品种子分别建立了鉴别模型。 对9个小麦样品共225个样本, 通过近红外光谱仪扫描获得从4 000～12 000 cm-1波段范围的光谱数据。 为了消除噪声, 对原始数据先进行了归一化预处理; 然后使用主成分分析(PCA)方法得到能反映小麦种子97.28%光谱信息的前10个主成分, 提高了数据处理效率; 最后利用仿生模式识别(biomimetic pattern recognition, BPR)方法建立小麦品种的鉴别模型。 对于每个样品中的25个样本, 随机挑选15个样本作为训练样本, 其余10个样本作为第一测试集, 其他品种共200个样本作为第二测试集。 在对第二测试集平均正确拒识率达到96.7%的情况下, 对第一测试集中的样本取得了95.6%的平均正确识别率。 实验结果表明, 该方法具有较高的鉴别准确度, 可以作为一种快速无损的转基因小麦种子鉴别方法。

A new method for the fast discrimination of varieties of transgene wheat by means of near infrared spectroscopy and biomimetic pattern recognition (BPR) was proposed and the recognition models of seven varieties of transgene wheat and two varieties of acceptor wheat were built. The experiment adopted 225 samples, which were acquired from nine varieties of wheat. Firstly, a field spectroradiometer was used for collecting spectra in the wave number range from 4 000 to 12 000 cm-1. Secondly, the original spectral data were pretreated in order to eliminate noise and improve the efficiency of models. Thirdly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 97.28%. Finally, the recognition models were established based on BPR. For the every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 200 samples of the other varieties were used as the second testing set. As the 96.7% samples in the second set were correctly rejected, the average correct recognition rate of first testing set was 94.3%. The experimental results demonstrated that the recognition models were effective and efficient. In short, it is feasible to discriminate varieties of transgene wheat based on near infrared spectroscopy and BPR.